Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo [Modélisation de la pression de la pollution par les nitrates en utilisant une approche statistique multivariables: the cas de la masse d’eau souterraine de Kinshasa, République Démocratique du Congo] [Modelagem do potencial de poluição por nitrato usando uma abordagem estatística multivariada: o caso dos aquíferos de Kinshasa, República Democrática do Congo] [Modelado de la presión de contaminación de nitrato utilizando un método estadístico multivariado: el caso del cuerpo de agua subterránea de Kinshasa, República Democrática del Congo]
Mfumu Kihumba A.,Catholic University of Louvain |
Mfumu Kihumba A.,Center Regional Detudes Nucleaires Of Kinshasa |
Ndembo Longo J.,Center Regional Detudes Nucleaires Of Kinshasa |
Vanclooster M.,Catholic University of Louvain
Hydrogeology Journal | Year: 2016
A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones. © 2015, Springer-Verlag Berlin Heidelberg.